The present surgical treatment for AF involves creating a myriad of incisions in the atrium to block the multiple macroreentrant circuits that were once thought to be the underlying mechanism. However, recently it has been shown that AF is often maintained by a single or small number of spatially stable focal sources. Unfortunately, present technologies of epicardial mapping are time-consuming and do not allow for a map-guided approach for surgical treatment. We propose to develop an algorithm to identify these unique substrates of AF in real time. Using information in a database of prior AF recordings, we will first evaluate AF using the Short Time Fourier transform, which allows rapid three-dimensional representation of a dominant frequency. The development of this algorithm will allow for a better understanding of the mechanisms of AF. After development of the algorithm, we will demonstrate the ability to map in real-time using a canine model of atrial fibrillation. We will next record data from human patients, and compare real-time analysis with off-line analysis to ensure accuracy (wave analysis will be weighed against the more time-consuming traditional manual editing technique). Finally, use of this real-time algorithm and the understanding gained of AF will allow for the development of a novel surgical approach using map-directed treatment.